The association of patient‐reported social determinants of health and hospitalization rate: A scoping review

Abstract Introduction The interplay between social determinants of health (SDOH) and hospitalization is significant as targeted interventions can improve the social status of the individuals. This interrelation has been historically overlooked in health care. In the present study, we reviewed studies in which the association between patient‐reported social risks and hospitalization rate was assessed. Method We performed a scoping literature review of articles published until September 1, 2022 without time limit. We searched PubMed, Embase, Web of Science, Scopus, and Google Scholar to find relevant studies using terms representing “social determinants of health” and “hospitalization.” Forward and backward reference checking was done for the included studies. All studies that used patient‐reported data as a proxy of social risks to determine the association between social risks and hospitalization rates were included. The screening and data extraction processes were done independently by two authors. In case of disagreement, senior authors were consulted. Results Our search process retrieved a total of 14,852 records. After the duplicate removal and screening process, eight studies met the eligibility criteria, all of which were published from 2020 to 2022. The sample size of the studies ranged from 226 to 56,155 participants. All eight studies investigated the impact of food security on hospitalization, and six investigated economic status. In three studies, latent class analysis was applied to divide participants based on their social risks. Seven studies found a statistically significant association between social risks and hospitalization rates. Conclusion Individuals with social risk factors are more susceptible to hospitalization. There is a need for a paradigm shift to meet these needs and reduce the number of preventable hospitalizations.


| INTRODUCTION
Hospitalization is one of the most expensive aspects of healthcare service, accounting for one-third of the total healthcare expenditure in the United States. [1][2][3] Many health and insurance organizations have attempted to minimize the hospitalization rate by implementing preventive policies, 4 with various clinical and epidemiological factors affecting this index. [5][6][7] In recent years, several studies have demonstrated the substantial impact of social determinants of health (SDOH) on hospitalization rates. [8][9][10][11][12][13][14][15] The World Health Organization (WHO) 16  The term "social risks" is used to describe adverse individual-level SDOH, such as food insecurity, unemployment, and housing instability. 17 Addressing SDOH not only helps prevent the occurrence of diseases but also promotes public health and social equity. 18 Although previous research has shown the relationship between healthcare utilization and social risks, 19 it has often been general and lacked details. For example, many studies merely examined the relationship between age, sex, and the insurance status of individuals with healthcare utilization. [20][21][22] It is also worth noting that the social construction of these characteristics, like the privileges and discriminations based on ageism, sexism, etc., has a much larger role in determining risk or protection than the characteristics themselves. The outlined approaches often lack a comprehensive evaluation of other social risks like food insecurity, neighborhood status, educational background, social isolation, and economic stability, which can provide a more accurate understanding of the social components that affect individuals' health.
While general demographic characteristics (age, gender, etc.) are worthwhile, they can lead to nonspecific and often less valuable findings for policymaking. Given the interplay and integrity of SDOH dimensions, evaluating their impact as a system will provide a more accurate assessment of their effect on hospitalization. 13 As the association between SDOH and hospitalization rate has gained attention recently and the potential size and scope of available literature and nature and extent of evidence were yet to be established, we used a scoping review design to elucidate the possible association between patient-reported SDOH and hospitalization rate.

| METHODS
The current scoping review followed the Joanna Briggs Institute (JBI) guidelines for scoping reviews 23,24 (Table 1) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). 25 The Institutional Review Board of Shiraz University of Medical Sciences assessed and approved the protocol of this study.

| Search strategy
The electronic PubMed, Embase, Web of Science, and Scopus databases were searched until May 1, 2022, using relevant keywords and MeSH terms representing "social determinants of health" and "hospitalization" in the titles and abstracts. An update search was also done on September 1, 2022. We performed forward and backward T A B L E 1 Methodological steps. Step Topic Description  (Table S1).

| Screening process
The initial citations were imported into Endnote X9 for screening, and duplicates were removed. The title and abstract of each study were reviewed separately by two authors (AA and RF). Then, the full texts were assessed for eligibility. The screeners were blinded to each other's decisions. During the first and second screening processes, all conflicts in decisions were resolved by consultation with a third author (KBL or STH).

| Eligibility criteria
For the inclusion of the studies, the participants, concept, and context were primarily defined (Table 1). We included studies in which (a) individuals reported their social needs (at least three aspects according to the framework described below), (b) the authors used quantitative investigation of the relationship between patientreported SDOH and hospitalization rate, and (c) the design of the study was community-based or hospital-based. We excluded studies in which (a) merely readmissions (planned or unplanned) were investigated, (b) studies that did not ask about social needs and used predefined data (like chart reviews) as a stand-in rather than patientreported data, and (c) conference reports, review articles, and commentaries. There was no exclusion owing to the age of the participants, sample size, date, language, or publication country.

| Data extraction, quality appraisal, and analysis
Using an Excel spreadsheet template, the data from the included studies were retrieved by two independent authors (AA and SS). The first author's surname, publication year, the year it was carried out, the purpose of the study, results, and conclusion were extracted.
Also, the quality of the studies was measured independently using the Joanna Briggs Institute (JBI) checklist according to the study designs. 26 The extraction of the data for two of the included studies was piloted to test spreadsheet usability before the main data extraction. Disagreements were solved by consensus with the author with the most expertise in the topic (KBL and SG).
We used thematic analysis to ascertain aspects of SDOH assessed in each study. 27 First, we reviewed the extracted articles and categorized the aspects of SDOH in each study as initial codes.
Then, we tried to fit the initial codes into the main themes of the Kaiser Family Foundation Framework (KFF) 28 for SDOH. In addition to KFF, two new themes emerged. Two external experts reviewed the final themes in addition to the authors. The relationship between each domain of SDOH, including odds ratios or prevalence of social risks, was also extracted.

| RESULTS
Our search yielded 14,852 citations. After duplicate removal (n = 3459) and screening, eight studies [8][9][10][11][12][13][14][15] met the criteria to be reviewed ( Figure 1). All of the studies were published from 2020 to 2022. All of the studies were undertaken in the United States. The sample sizes ranged from 226 9 to 56,155 15 participants. Key study findings are summarized in Table 2. Two studies 10,15 investigated the impact of the cumulative number of SDOH on hospitalization, while another two 9,14 determined the effect of each SDOH domain on hospitalization rate, and one 13 investigated both. In three studies, 8,11,12 latent class analysis was applied to divide participants based on their social risks. In terms of study design, six 8,10,12-15 were crosssectional, and two 9,11 were retrospective cohort studies. The quality of the included studies is reported in Table S2. Also, the cumulative impact of SDOH was investigated by Jones et al. 10 8 The authors examined veterans at high risk for hospitalization through a mail survey on SDOH. The research team used eleven self-reported items known to impact hospital admission and to be sensitive to the intervention to classify participants based on their social risk using latent class analysis. (n = 4684) Applying latent class analysis, five subgroups were identified: "minimal SDOH vulnerabilities" (8% hospitalization rate), "poor/fair health with few SDOH vulnerabilities" (12% hospitalization rate), "social isolation" (10% hospitalization rate), "multiple SDOH vulnerabilities" (12% hospitalization rate), and "multiple SDOH vulnerabilities without food or medication insecurity" (10% hospitalization rate). The "Multiple SDOH vulnerabilities" subgroup showed a higher risk of 180-day hospitalization than those with "minimum SDOH vulnerabilities" (OR: 1.53).
Canterberry et al. The ED visit rate was 55% (mean: 1.5 per year). The incidence of hospitalization was 20% (mean: 0.4 per year). Patients with a history of "unaddressed housing insecurity" (rate ratio: 1.55) or a "safety concern" (rate ratio: 2.04). had a higher annual ED usage rate among persons who had >0 ED Visits or Hospitalizations. A retrospective analysis of Medicaid beneficiaries utilizing a combination of patient-reported SDOH and Medicaid claims. By latent class analysis, participants were divided into four social risk classes. (n = 8943) With each higher (worse) social risk class, the adjusted log relative rates of both primary care visits and visits to the ED were higher. Participants who were "unemployed and had many social risks" (the highest social risk class) had a log relative primary care treatable rate of 39% and a log relative need for ED care rate of 29%, after adjusting for age, gender, and severity of illness.

Rogers et al. (2020) 12
Social needs were screened among a population of predicted high healthcare utilizers. Latent class analysis was applied to categorize the participants based on their reported SDOH. (n = 2,533) Participants were separated into four social risk classes based on latent class analysis. Class 1 consisted of people with four or more self-reported risks, and class 4 consisted of participants with no self-reported risks. Despite having a lower Charlson comorbidity score, class 1 patients had considerably more total inpatient visits than class 4 patients (1.  34,35 as demonstrated in the included studies. 13,15 As mentioned in the results section, some studies analyzed the cumulative impact of the number of social needs, while others used latent class analysis to examine social needs as a whole.
While every SDOH need is important, it may be more beneficial to examine a constellation of social needs since it paints a more holistic picture. 13 However, there is a paucity of studies investigating SDOH as a whole; it is suggested to collect the SDOH needs of individuals before or during healthcare utilization. 36 All of the included studies investigated the impact of food security issues on hospitalization rates. hospitalization.
An ethical consideration arising from the patient-reported SDOH is collecting patient information on some issues that healthcare organizations cannot address. 8 Although healthcare organizations can work to dismantle institutional racism and provide equitable care (considering racism as an example of a social risk), most healthcare organizations are not capable of addressing housing insecurities or neighborhood problems. As a result, there should be a careful evaluation of the research projects in this regard to maintain patients' information privacy. It is also worth noting that some countries seek to address non-healthcare-related factors that influence health outcomes through "social prescribing", which is described elsewhere. 42,43 There is evidence suggesting that patients with social risks do not want assistance from the healthcare system. 44

DATA AVAILABILITY STATEMENT
All of the extracted data are represented within the manuscript.

TRANSPARENCY STATEMENT
The lead author Ali Ardekani affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.